Try Letta
Overview
Letta comes out of the MemGPT research, which proposed treating a language model's context window the way an operating system treats RAM: a scarce resource with an explicit hierarchy, where something decides what stays resident and what gets paged out to storage and recalled later. Letta is that idea as a working agent runtime.
The consequence is a different architecture from bolt-on memory. Agents are stateful objects that persist between sessions and manage their own context — deciding what belongs in the active window, what moves to long-term storage, and what to pull back when it becomes relevant again. For an agent that runs for weeks across hundreds of interactions, that self-management is a more principled answer than retrieving from an external store on every turn.
The cost is conceptual. Letta asks you to think about agents as persistent processes with memory hierarchies, which is a genuine shift from the request-response mental model most teams start with. It is open source and the research pedigree is real, but it is a more demanding tool than a memory layer you add to an agent you already have.
Key Features
Agent-Managed Context
Agents decide what stays in the active context window and what moves to storage, rather than a framework guessing on their behalf.
Tiered Memory Hierarchy
Explicit tiers between active context and long-term storage, borrowed conceptually from operating system memory management.
Persistent Stateful Agents
Agents exist between sessions as durable objects, which is the right model for assistants that run for weeks rather than minutes.
Open Source with Research Lineage
Built by the MemGPT team, with the underlying approach published and inspectable rather than proprietary.
Inspectable Memory State
You can examine what an agent currently believes and remembers, which makes debugging long-lived agents tractable.
Tool and Framework Integration
Agents call external tools and integrate with the surrounding stack rather than being a closed system.
Pros & Cons
Advantages
- The most principled architecture for genuinely long-running agents
- Research foundations rather than a memory feature retrofitted onto a framework
- Inspectable memory state makes long-lived agents debuggable
- Open source
- Stateful agents match how assistants are actually used
Disadvantages
- Steeper conceptual learning curve than a bolt-on memory layer
- Requires adopting Letta's agent model rather than adding to your own
- Smaller ecosystem than Mem0
- Overkill for short-lived or single-session agents
Pricing Plans
| Plan | Price | Key Features |
|---|---|---|
| Open Source | Free | Self-hosted runtime, bring your own models |
| Cloud | Usage-based | Managed hosting for stateful agents |
| Enterprise | Custom | Support, compliance and deployment assistance |
Best Use Cases
Letta Excels At:
- Assistants that run for weeks or months across many sessions
- Agents where context management is the actual engineering problem
- Teams who want to reason explicitly about what an agent remembers
- Research and advanced agent work
May Not Be Ideal For:
- Simple single-session assistants
- Teams that want to add memory to an existing agent without restructuring
- Projects needing the largest possible ecosystem
How It Compares
Letta vs Mem0
Mem0 adds memory to the agent you already have; Letta asks you to build the agent its way and gives you a much more principled memory model in return. Mem0 for pragmatism, Letta when context management is the core problem.
Letta vs a framework plus vector store
Assembling memory from a framework and a vector database gives you control and a lot of decisions to make correctly. Letta encodes those decisions, which is valuable if you agree with them and constraining if you do not.
Final Verdict
Our Recommendation
Letta is the thoughtful choice for agents that genuinely live a long time. Treating the context window as a managed resource with an explicit hierarchy is the right abstraction, and the MemGPT lineage means it comes from people who studied the problem rather than patched around it. It asks more of you than Mem0 — a different mental model and a commitment to its agent runtime — so it earns its place when context management is the hard part of your system rather than an afterthought. For a chatbot that needs to remember a user's name, it is more machinery than the job needs.